The automotive industry has become a robust manufacturing sector in the global manufacturing field, boosting and developing at high speed based on technology updates, environmental protection rules and regulation changes, and customers' new needs and demands. Such dynamics give rise to complex problems like optimizing cost strategy and innovation, operating under high sustainability standards, and navigating the supply chain. These difficulties have led to the need for more structured, logical and effective approaches to decision-making that can handle cross purposes and the world of slow changes and unpredictability. To mitigate these problems, in this context, this study presents the Circular Intuitionistic Fuzzy EDAS (CIF-EDAS) approach to handle decision-making challenges in the automotive industry. Integrating the circular intuitionistic fuzzy sets (CIFSs) with the EDAS approach provides a better assessment of uncertainty and hesitation, enhancing the reliability and strength of multi-criteria decision-making (MCDM) methods. The applicability of the proposed approach is illustrated by a case study in the automotive industry, in which the assessment of different scenarios based on conflicting criteria is effective. Moreover, a comparison analysis has been conducted, demonstrating the proposed approach's superiority and highlighting its efficacy in facilitating robust decision-making. The CIF-EDAS approach is clarified to exemplify its applicability in optimizing decision-making of complicated real-life industrial problems and demonstrating its capability to evolve for the automotive industry's newly emergent restrictive and dynamic demand.

Martial arts teaching skills involve the systematic instruction of physical techniques, mental discipline, and tactical strategies to develop a student’s proficiency in self-defense and personal growth. It is relevant when there is a specific analysis of the diverse features involving the technique, versatility, and interaction features. In Multi-Attribute Decision-Making (MADM), conventional approaches cannot provide a framework to capture the uncertainties and vague judgments inevitably associated with global experts’ evaluations of such a broad spectrum of skills. These limitations are seen when the evaluations do not consider the dynamism, uncertainty, or subjective nature inherent in the evaluation process, thus having less reliable or less complete decision outcomes. To handle these challenges, the concept of Complex Picture Fuzzy Sets (C-PFS) is utilized, and new advanced Dombi aggregation operators (AO), including Complex Picture Fuzzy Dombi Weighted Averaging (C-PFDWA) and Complex Picture Fuzzy Dombi Weighted Geometric (C-PFDWG) operators are developed. These operators can be effectively utilized to overcome real-life evaluation issues in martial arts skills assessment, serving as intelligent algorithms that facilitate more accurate decision-making processes. To allow the collection of the judgments of different experts over the membership, abstention, and non-membership degrees, offering a comprehensive and detailed picture of an instructor's performance even in cases where there is uncertainty. This comprehensive approach ensures a more reliable and customized assessment, enhancing instructional quality and better student outcomes as instructors receive more targeted feedback on improvement areas. In addition, this study includes detailed simulations and complete results of selected parameters, which demonstrate the practical applicability and robustness of the proposed operators, thus providing strong justification for the research and validating the efficacy of the developed framework.

The impact of airborne pollutants present in the environment, entering the body through breathing, can cause significant risks of respiratory and heart-related health problems for individuals. For this, different air purifiers are commonly used to eliminate delicate particulate matter PM_2.5, and various studies have examined their effectiveness. This paper aims to analyze airborne pollutants and, by considering them, assess the performance of air purifiers in reducing the concentration of air pollutants in the environment. The aggregation operator (AO) plays a significant role in aggregating the multiple criteria and gives us a result in a singleton set, which assists us in decision-making (DM). So, considering this, the Heronian mean (HM) operator and its special cases such as averaging and geometric operators have been used in this paper. Moreover, circular intuitionistic fuzzy (C-IF) theory is more efficient and comprehensive than the intuitionistic fuzzy set (IFS), as the standard IFS cannot cope with the problems within a circular environment. So, the HM operator under the C-IF environment, such as circular intuitionistic fuzzy Heronian mean (C-IFHM) and it’s averaging and geometric operator, has been presented. Further, some prevalent and crucial properties and theorems have been defined. Then, an algorithm was developed to solve a multicriteria decision-making (MCDM) problem by using the proposed operators within the circular environment. To validate the effectiveness and priority, this paper presents a numerical example of MCDM, and a comparison analysis was conducted to verify the practicality of the proposed approach.

A single-valued Neutrosophic hesitant fuzzy set (SVNHFS) is a combination of a single-valued neutrosophic set (SVNS) and hesitant fuzzy set (HFS) that has been developed to address insufficient, unreliable, and vague environments in which each element has several possible options determined by the truthiness, indeterminacy and falsity value. By considering this, in this paper, we have proposed the Aczel-Alsina aggregation operator (AAAO) for SVNHFS, which is more flexible t-norm and t-conorm than the other and due to the flexible nature of parameters to solve Multi-Attribute decision making (MADM) problems. Further, the score function, accuracy function, and certainty function of SVNHFS have been defined. In this paper, we proposed the single-valued neutrosophic hesitant fuzzy Aczel-Alsina Weighted Averaging Operator (SVNHFAAWA), single-valued neutrosophic hesitant fuzzy Aczel-Alsina Weighted ordered Averaging Operator (SVNHFAAWOA), and single-valued neutrosophic hesitant fuzzy Aczel-Alsina hybrid averaging operator (SVNHFAAHA). To testify to the reliability and stability of the newly created aggregation operator (AO), an application of MADM has been discussed.

In today’s digital era, university students’ career-related decision-making has become increasingly complex, shaped not only by personal aspirations and societal expectations but also by the algorithmic recommendations embedded in AI-supported technologies. While such systems provide convenient references, their outputs are often vague, generalized, or contextually limited, which underscores the irreplaceable role of human judgment in situations of uncertainty. Against this backdrop, the present study develops a multi-criteria decision-making (MCDM) framework tailored to the context of student management and career guidance. Specifically, it integrates the compromise ranking strategy CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) with the expressive flexibility of Circular Pythagorean Fuzzy Sets (Cir-PyFS). Cir-PyFS was chosen to reflect the modalities of non-decision, inexactness, and subjective desire in human evaluation that people may have. The linguistic assessments of each alternative were redefined in CPFS representations within the proposed Circular Pythagorean Fuzzy-based CRADIS model (Cir-PyFBCM), which incorporates membership, non-membership, and radius values. The model outperformed traditional fuzzy MCDM approaches in terms of stability, interpretability, and sensitivity to the influence of decision-makers in an empirical exercise using a realistic career-planning example with several alternative courses of action, multiple expert decision-makers, and eight benefit-based evaluation criteria. These comparison and sensitivity analysis results support that the proposed methodology serves as an effective bridge, linking mathematical precision with the human approach to reasoning, and offers a more careful, flexible, and reliable tool for governing high-stakes decisions, especially university students, when making their career decisions in AI-supported realms. More broadly, the study underscores the potential of responsible AI-assisted decision-making in promoting reflective, evidence-based practices within higher education management.

Cold emailing is the most popular technique for B2B lead generation, but it is hard to select the best option due to uncertainty, subjective judgment, and the dynamic behaviour of users. Conventional analytical models usually do not recognize qualitative aspects such as personalization, timing sensitivity, and user perception and hence provide inconsistent decision-making. To overcome this shortcoming, this paper presents a new decision-making model that incorporates circular q-rung orthopair fuzzy sets (Cq-RoFS) combined with the MAIRCA approach. The proposed method is useful in modelling uncertainty, hesitation, and linguistic ambiguity, as well as offering a deviation-based comparative analysis of options. A study is carried out in a SaaS setting where five cold email strategies are assessed using four benefit criteria. The judgments of the experts, in the form of linguistic expressions, are converted into Cq-RoFS values and processed within the suggested framework. The findings show that the hybrid semi-automated approach has the lowest degree of deviation, which means that it is the most efficient one, followed by AI-adaptive and bulk automated strategies. The sensitivity analysis of parameter q at different values also proves that the model is stable and robust, and no rank reversal has been noted. The research paper reveals the necessity of a balance between personalization and automation in cold email campaigns and shows that the proposed framework is superior in its ability to manage complex and uncertain decision-making conditions. The Cq-RoFS MAIRCA model enhances the evolution of fuzzy multi-criteria decision-making and provides a useful, dependable, and comprehensible instrument to streamline digital marketing initiatives.